Robust scalable and adaptive frequency estimation and frequency tracking for wireless systems

ABSTRACT

Methods and apparatuses to determine a frequency adjustment in a mobile wireless device are disclosed. A method includes determining a coarse frequency error estimate and multiple fine frequency error estimates; selecting at least one candidate fine frequency error estimate having a frequency value closest to a corresponding frequency value for the coarse frequency error estimate; and determining a frequency adjustment based on a combination of the coarse frequency error estimate and the selected at least one candidate fine frequency error estimate. In an embodiment, the method further includes calculating a confidence metric for the coarse frequency error estimate; when the confidence metric exceeds a threshold value, determining the frequency adjustment based on the candidate fine frequency error estimate; otherwise, determining the frequency adjustment based on a fine frequency error estimate in the plurality of fine frequency error estimates closest to a most recent previous fine frequency error estimate.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.61/716,453, filed Oct. 19, 2012, entitled “SCALABLE AND ADAPTIVEFREQUENCY ESTIMATION AND FREQUENCY TRACKING FOR WIRELESS SYSTEMS,” andU.S. Provisional Application No. 61/810,206, filed Apr. 9, 2013,entitled “ROBUST SCALABLE FREQUENCY ESTIMATION AND FREQUENCY TRACKINGFOR WIRELESS SYSTEMS,” both of which are incorporated by referenceherein in their entireties for all purposes.

TECHNICAL FIELD

The described embodiments relate generally to wireless systems and moreparticularly to frequency estimation and tracking algorithms thatcombine fine frequency estimation results and coarse frequencyestimation results to improve frequency estimation and tracking accuracyand stability.

BACKGROUND

Frequency tracking loops are commonly employed in wireless systems toalign a receiver frequency clock to a transmitter frequency clock, or toadjust received signals to account for frequency differences between atransmitter, e.g., in a wireless access network base station, and areceiver, e.g., in a mobile wireless device. Frequency tracking loopscan be used to improve accuracy when recovering a signal from a noisycommunication channel and can also be used to distribute clock timinginformation to properly align frequency sampling clock timing pulses indigital logic designs. Variability of a frequency of a crystaloscillator (XO) clock source in a receiver, however, can result infrequency errors with respect to a transmitted carrier frequency thatcan lead to a relative frequency error between the clock source used inthe receiver of a mobile wireless device and a clock source used in atransmitter of a base station in a wireless access network. Thesefrequencies errors can impact the performance of reception and decodingof the received signals in the mobile wireless device, and can therebydecrease the reliability and functionality of the wireless system.

Frequency errors can affect different types of wireless systems that usedifferent radio access technologies, including code division multipleaccess (CDMA) wireless systems and orthogonal frequency divisionmultiplexed (OFDM) wireless systems. In CDMA-based wireless systems, afrequency error can deteriorate the quality of received signals.Moreover, a carrier frequency error in the receiver can translate tosampling timing errors that can accumulate over time, which can breakthe orthogonality of spreading codes used in CDMA-based wireless systemsto differentiate signals sent to different mobile wireless devices.Orthogonality of signals provided by code division multiplexing inCDMA-based wireless systems can be critical for a receiver in a mobilewireless device to separate signals intended for the mobile wirelessdevice from signals intended for other mobile wireless devices. Loss oforthogonality can increase an amount of interference generated by“non-orthogonal” signals that can be simultaneously received with asignal intended for a particular mobile wireless device in a CDMA-basedwireless system, thereby affecting received signal performance in theparticular mobile wireless device. Similarly, a carrier frequency errorcan affect the orthogonality of different sub-channels used in anOFDM-based wireless system, and thereby can increase inter-channelinterference (ICI) between sub-channels of the OFDM-based wirelesssystem and deteriorate the overall performance of the OFDM-basedwireless system.

Accurate and robust frequency estimation and frequency tracking can becritical for designing receivers for mobile wireless devices thatreliably correct for frequency errors. Different frequency errorestimation and tracking schemes can be used that provide a balancebetween accuracy, convergence time, and frequency pull-in range. Achannel impulse response (CIR) can be estimated based on a set ofreceived pilot symbols and/or a set of received data symbols, and theresulting CIR can be used to characterize phase information of awireless communication channel between the mobile wireless device andthe base station of a wireless access network. The phase informationembedded in the CIR can include an amount of accumulated frequency errordue to differences in clock frequencies at a source (e.g., the basestation) and a sink (e.g., the mobile wireless device). CIR techniquescan be accurate but require longer convergence time, higher computingpower (which can result in higher power consumption), and narrowerfrequency pull-in range. Alternatively, a technique that exploits acontinuous phase ramping between different samples of a symbol, e.g.,due to frequency errors, can be used when frequency error estimationwith a larger pull-in range than afforded by a CIR based frequency errorestimation method is needed. Each frequency error estimation andtracking technique can be better suited to certain received signalsdepending on whether a higher accuracy, narrower pull-in range, “fine”frequency tracking loop or a lower accuracy, wider pull-in range,“coarse” frequency tracking loop is required.

Therefore, what is desired is an algorithm that combines fine frequencyestimation results and coarse frequency estimation results and employsmethods having different precision and complexity to improve both theaccuracy and robustness of frequency estimation and frequency trackingin wireless systems.

SUMMARY OF THE DESCRIBED EMBODIMENTS

In an embodiment, a method to estimate frequency errors and determine afrequency adjustment in a mobile wireless device is disclosed. Themethod includes at least the following steps: (1) determining a coarsefrequency error estimate, (2) determining a plurality of fine frequencyerror estimates, (3) selecting at least one candidate fine frequencyerror estimate in the plurality of fine frequency error estimates, theat least one candidate fine frequency error estimate having a frequencyvalue that is closest to a corresponding frequency value for the coarsefrequency error estimate, and (4) determining a frequency adjustmentbased on a combination of the coarse frequency error estimate and theselected at least one candidate fine frequency error estimate. In someembodiments, the method further includes (5) calculating a confidencemetric for the coarse frequency error estimate, (6) comparing theconfidence metric to a pre-determined threshold value, (7) when theconfidence metric exceeds the pre-determined threshold value,determining the frequency adjustment based on the candidate finefrequency error estimate, and (8) when the confidence metric does notexceed the pre-determined threshold value, determining the frequencyadjustment based on a fine frequency error estimate in the plurality offine frequency error estimates that is closest to a most recent previousfine frequency error estimate. The resulting algorithm can effectivelycombine fine and coarse frequency estimation results with differentprecision and complexity to improve the accuracy and robustness offrequency estimation and tracking in wireless systems.

In another embodiment, a mobile wireless device includes wirelesscircuitry including at least one transceiver, one or more processorscoupled to the wireless circuitry, and a memory coupled to the one ormore processors. The one or more processors are configured to executecomputer-executable instructions stored within the memory to cause themobile wireless device to determine a coarse frequency error estimate,to determine a plurality of fine frequency error estimates, to calculatea confidence metric for the coarse frequency error estimate, to comparethe confidence metric to a threshold value, when the confidence metricexceeds the threshold value, to determine a frequency adjustment basedon a candidate fine frequency error estimate in the plurality of finefrequency error estimates that is closest to the coarse frequency errorestimate; and when the confidence metric does not exceed the thresholdvalue, to determine the frequency adjustment based on a fine frequencyerror estimate in the plurality of fine frequency error estimatesclosest to a most recent previous fine frequency error estimate.

In a further embodiment, a non-transitory computer readable mediumhaving computer program encoded thereon is disclosed. The computerprogram code, when executed by one or more processors, causes a mobilewireless device to perform the operations of determining a coarsefrequency error estimate, determining a plurality of fine frequencyerror estimates, selecting at least one candidate fine frequency errorestimate in the plurality of fine frequency error estimates, the atleast one candidate fine frequency error estimate having a frequencyvalue that is closest to a corresponding frequency value for the coarsefrequency error estimate, and determining a frequency adjustment basedon a combination of the coarse frequency error estimate and the selectedat least one candidate fine frequency error estimate.

BRIEF DESCRIPTION OF THE DRAWINGS

The described embodiments and the advantages thereof may be understoodby reference to the following description in conjunction with theaccompanying drawings. The drawings provided herein do not limit anychanges in form and detail that may be made to the describedembodiments.

FIG. 1 illustrates a block diagram of a generic frequency tracking loopin accordance with some embodiments.

FIG. 2 illustrates a block diagram of a dual frequency tracking loopthat combines fine frequency estimation and coarse frequency estimationin accordance with some embodiments.

FIG. 3 illustrates a graph demonstrating a combination of fine frequencyestimation and coarse frequency estimation in accordance with someembodiments.

FIG. 4 illustrates another graph demonstrating a combination of finefrequency estimation and coarse frequency estimation in accordance withsome embodiments.

FIG. 5 illustrates a flowchart for a method to combine fine frequencyand coarse frequency estimation and tracking in accordance with someembodiments.

FIG. 6 illustrates a flowchart for another method to combine finefrequency and coarse frequency estimation and tracking in accordancewith some embodiments.

FIG. 7 illustrates a diagram of a representative set of elementsincluded in a mobile wireless device in accordance with someembodiments.

DETAILED DESCRIPTION OF SELECTED EMBODIMENTS

Representative applications of methods and apparatus according to thepresent application are described in this section. These examples arebeing provided solely to add context and aid in the understanding of thedescribed embodiments. It will thus be apparent to one skilled in theart that the described embodiments may be practiced without some or allof these specific details. In other instances, well known process stepshave not been described in detail in order to avoid unnecessarilyobscuring the described embodiments. Other applications are possible,such that the following examples should not be taken as limiting.

In the following detailed description, references are made to theaccompanying drawings, which form a part of the description and in whichare shown, by way of illustration, specific embodiments in accordancewith the described embodiments. Although these embodiments are describedin sufficient detail to enable one skilled in the art to practice thedescribed embodiments, it is understood that these examples are notlimiting; such that other embodiments may be used, and changes may bemade while remaining within the scope of the described embodiments.

Receivers in a mobile wireless device can use a crystal oscillator (XO)clock source, which in some embodiments can include a voltage controlledcrystal oscillator (VCXO) having an adjustable clock source frequency.The clock source can determine a frequency at which analog radiofrequency (RF) waveforms received by the mobile wireless device aresampled and converted to a set of digital samples for demodulation anddecoding by wireless circuitry in the mobile wireless device. A receivercan be referred to also, in some embodiments, as a receive signal chain,and a portion of a transceiver (transmitter/receiver combination).Sampling the received waveforms at an accurate frequency rate and phasecan be required to maximize a signal to noise plus interference ratio(SINR) in the receiver of the mobile wireless device. The mobilewireless device can estimate and track frequency errors to provide acorrection to the VCXO in an adaptive feedback loop. In someembodiments, receivers in the mobile wireless device can operate at anominal “fixed” frequency and subsequent frequency adjustments can beapplied to the digital received samples to compensate for clockfrequency differences between the receiver in the mobile wireless deviceand the transmitter in the base station of the wireless access network.The adjustment of the digital samples can be accomplished, in someembodiments, by applying an appropriate frequency rotation to thedigital samples to correct for the sampling frequency error. The clocksource frequency in the receiver of the mobile wireless device can alsovary over time due to changes in operating conditions, e.g., ascomponent temperatures vary due to changes in ambient temperature aswell as changes in power consumption of components in the mobilewireless device. The mobile wireless device can include one or morefrequency tracking loops by which frequency differences between a clocksource used by the receiver in the mobile wireless device and a clocksource used by the transmitter in the wireless access network basestation can be estimated and tracked, with subsequent adjustmentsdetermined and applied to compensate for the estimated frequency errors.

Several different frequency error estimation methods can be used in amobile wireless device, each frequency error estimation method usedalone or in combination. Different frequency error estimation methodscan require different amounts of information, different amounts ofcomputational complexity, and/or different amounts of time to computefrequency error estimates from a set of received digital samples.Different frequency error estimation methods can also result infrequency error estimates that have different levels of accuracy or canprovide for correction of frequency errors over different ranges of“pull-in.” In some embodiments, a frequency error estimation algorithmcan determine receiver sampling frequency errors based on decoding aknown transmitted sequence, e.g., a pilot sequence. Differences betweena received version of the pilot sequence and a “known” version of thepilot sequence can be used to compute a channel impulse response (CIR),which can characterize a downlink (DL) communication channel (alsoreferred to as a path) between a base station in the wireless accessnetwork from which the pilot sequence originates and the mobile wirelessdevice receiving the pilot sequence. Changes in CIR estimates overrelatively short periods of time (during which the communication channelcan be considered approximately stationary) can be used to characterizefrequency errors between the receiver of the mobile wireless device andthe transmitter of the base station in the wireless access network.Successive CIR estimates can include accumulated phase shifts that canbe used to estimate frequency errors. In other embodiments, actualdecoded data sequences can be used to determine CIR estimates from whichfrequency error estimates can be calculated. Each frequency errorestimation algorithm can provide a different frequency “pull-in” rangeand a different confidence limit for accuracy of the frequency errorestimates provided. In addition, a simple linear combination or aweighted linear combination of frequency error estimations computed fromdifferent frequency error estimation algorithms may not provide asufficiently accurate frequency error estimate or may require a longerthan desired time for an associated frequency tracking loop to convergein order to determine an accurate frequency error estimate. Therefore,in order to better exploit different pull-in frequency ranges andvariable frequency error estimation accuracies provided by differentfrequency error estimation methods, it can be advantageous to combinemultiple frequency error estimation methods into a common algorithm thatis scalable, adaptive and robust enough to operate under varying signalquality reception conditions as described further herein. A robustfrequency error estimation algorithm can provide improved receiverperformance under certain adverse received signal conditions, e.g., whenhigh levels of interference or a relatively low signal to interferenceplus noise ratio (SINR) exist at the receiver of the mobile wirelessdevice. Representative pilot sequences (or more generally, “known”transmit sequences) can include cell-specific reference signals providedon a set of sub-carriers in particular OFDM symbols of sub-framestransmitted by an evolved NodeB (eNB) of a Long Term Evolution (LTE)wireless network. In some embodiments, the mobile wireless device usesreference signal information provided in a single OFDM symbol todetermine a “coarse” frequency error estimate and reference signalinformation provided in at least two different OFDM symbols to determinea set of “fine” frequency error estimates.

FIG. 1 illustrates a diagram 100 of a generic frequency tracking loopthat can be used to estimate a frequency error and to provide afrequency adjustment to compensate for frequency errors in a receiver ofa mobile wireless device in a representative wireless system. A set ofreceived digital samples, e.g., obtained from an analog to digitalsampling conversion process (not shown) performed on radio frequencywaveforms, can be input to a frequency rotator block 102 that can adjustthe received digital samples based on an estimated clock frequencydifference between the receiver of the mobile wireless device and atransmitter of the wireless access network base station. The output ofthe frequency rotator block 102 can provide a stream of frequencyadjusted received digital samples that can be input to a frequency errorestimation block 104. In some embodiments, the frequency errorestimation block 104 can use a set of pilot samples, e.g., digitalsamples extracted from received pilot symbols that are transmittedaccording to a pre-determined (i.e., known) pattern, to estimate afrequency error. In some embodiments, the frequency error estimationblock 104 uses a set of pilot samples contained in a single pilot symbolor within a set of related pilot symbols. In some embodiments, thefrequency error estimation block 104 uses information from multiplepilot symbols separated in time by data symbols. In other embodiments,the frequency error estimation block 104 can use a set of data samples,e.g., digital samples extracted from random data signals afterdemodulation and decoding, to estimate the frequency error. Thus, theerror estimation algorithm used in the frequency error estimation block104 can be based on a known pilot sequence or based on random data. Aloop gain 106 can be applied to the frequency error estimates outputfrom the frequency error estimation block 104, and a sequence of loopgain adjusted frequency error estimates can be filtered through afrequency error estimation filtering block 108. The filter block 108 cancontrol a bandwidth of the frequency error estimate signal and canminimize added noise to provide a stable and converging frequency errorestimate signal. The filtered frequency error estimate signal outputfrom the filter block 108 can be converted to an appropriate controlword in a conversion block 110 to provide a compensation adjustmentsignal to apply to the frequency rotator block 102. The compensationadjustment signal can update the frequency rotator block 102 thatfrequency rotates (i.e., phase adjusts) the received digital samples tocompensate for frequency differences between the mobile wirelessdevice's receiver frequency and the wireless access network basestation's transmitter as determined by the frequency error estimationblock 104. The set of blocks illustrated in FIG. 1 illustrate a feedbackloop that can estimate and compensate for frequency errors. In someembodiments, the feedback loop 100 illustrated in FIG. 1 is a firstorder filtered frequency tracking feedback loop. In some embodiments, afrequency compensation adjustment can be applied directly to a clocksource (not shown), e.g., a VCXO, in the receiver of the mobile wirelessdevice rather than to a frequency rotator block 102. Thus the samplingfrequency of the VCXO clock source can be adjusted to provide differentsampling frequencies that generate the received digital samples (and thefrequency rotator block 102 can be redundant or not required in someembodiment). The frequency compensation adjustment can alter thesampling frequency used by the receiver to more closely match thetransmitter frequency as required.

The frequency error estimation block 104 illustrated in FIG. 1 canestimate a frequency error based on a single received pilot sequence, aportion of a single received pilot sequence, a set of two or morereceived pilot sequences, or based on data sequences. One method toestimate the frequency error can be based on changes observed in channelimpulse response (CIR) estimates derived from received pilot sequencesor from received data sequences. A channel impulse response cancharacterize a communication channel, i.e., the CIR can represent acomplex-valued multi-path profile for a communication path from a basestation transmitter to a mobile wireless device receiver. The mobilewireless device can estimate the CIR upon an initial connection andduring a connection, as changes in the communication channel between themobile wireless device and the base station of the wireless accessnetwork can vary over time. For relatively short periods of time,however, the CIR can be considered approximately stationary (i.e., witha set of unchanging characteristics over the short time interval). Themobile wireless device can measure changes in the CIR over the shorttime interval and can attribute changes in the CIR at least in part to adifference in sampling frequency between the transmitter used at thebase station and the receiver used in the mobile wireless device. Eachsuccessive CIR estimate can include embedded phase information that isbased on an accumulated frequency error. By comparing phase informationcontained in two different CIR estimates separated in time, an estimateof the frequency error can be derived. CIR estimation is commonly basedon an analysis of received known pilot sequences, e.g., using a sequenceof received pilot symbols that can be de-scrambled in the frequencydomain and converted into the time domain using an inverse fast Fouriertransform (IFFT). For a communication channel that is approximatelystationary (i.e., not time varying) over a time interval that includestwo different received pilot sequences (or data sequences), if there isno frequency error between the transmitter in the wireless accessnetwork base station and the receiver of the mobile wireless device, theCIR values derived from two different pilot sequences (or datasequences) should be the same (in the absence of added noise and/orinterference). With a difference in frequency between the transmitter inthe base station of the wireless access network and the receiver of themobile wireless device, however, the CIR values can vary. A frequencyerror estimation and tracking algorithm in the mobile wireless deviceuse the changing CIR values to determine a frequency error estimate andto track changes in frequency over time. In some embodiments, CIRestimation can also be accomplished using a sequence of received datasymbols. For example, an estimate of transmitted data symbols can bedetermined using equalization and hard decision decoding at the receiverof the mobile wireless device, and a received data symbol sequence canbe compared with the estimate of transmitted data symbols to determine achannel impulse response. As frequency error information can be embeddedin the estimated CIR through an accumulated frequency error inducedphase offset, an estimate of the frequency error can be determined bycomparing different CIR estimates derived from data symbol sequences(just as also can be determined using CIR estimates derived from pilotsymbol sequences). An effective discriminant to estimate the frequencyerror can be constructed by correlating CIR estimates derived from twodifferent pilot sequences separated in time, or from two different datasequences also separated in time.

As a representative exemplary embodiment, two different CIR estimates,derived from two different pilot sequences, can be correlated to extractphase difference information. In a representative embodiment, adiscriminant can be determined using Equation (1).

C _(i) *C _(i+1) =∥S∥ ² e ^(j2πf) ^(e) ^(ΔT)   (1)

where C_(i) can represent a vector of the estimated CIR based on thei^(th) pilot sequence, and each element of C_(i) can correspond to adifferent complex-valued channel tap derived from the i^(th) pilotsequence. Similarly, C_(i+1) can represent a vector of the estimated CIRbased on the i+1 ^(th) pilot sequence. The operator * can indicate aHermitian transpose of a vector two which it is applied. Multiple tapsof the CIR can provide higher processing gain, as each tap of the CIRcan provide information for a different path of the multi-path channelbetween the base station and the mobile wireless device. Equation (1)represents an inner product of the two CIR vectors C_(i) and C_(i+1).The first term on the right hand side of Equation (1), ∥S∥², canrepresent an “energy” of the pilot sequence. The second term canrepresent a complex-valued phase shift corresponding to a frequencyerror fe and a time separation ΔT between the two CIR vectors C_(i) andC_(i+1). In a representative embodiment, the mobile wireless device andthe base station of the wireless access network can operate inaccordance with a Long Term Evolution (LTE) wireless communicationprotocol, e.g., as published by the 3^(rd) Generation PartnershipProject (3GPP) standardization group. In a representative embodiment,pilot sequences are transmitted by the base station of the wirelessaccess network every 5 milliseconds.

In a representative embodiment, a filtered discriminant d_(f) _(e) canbe determined using Equation (2).

d _(f) _(e) =Σ_(i)∝_(i) C _(i) *C _(i+1)   (2)

Equation (2) includes a weighting factor ∝_(i) by which thediscriminants of Equation (1) can be combined to provide a filtereddiscriminant value. A high processing gain for the discriminantcalculation of Equations (1) and (2) can depend on a length of the pilotsequence, e.g., longer pilot sequences can provide more information andtherefore a higher processing gain than shorter pilot sequences. Afrequency pull-in range for the frequency error estimate can depend on atime difference (ΔT) between successive pilot sequences, each pilotsequence providing an independent CIR estimate used in the discriminantEquations (1) and (2). In particular, the frequency pull-in range can beinversely proportional to the time difference ΔT, i.e., directlyproportional to 1/ΔT, so that more widely separated CIR estimates(higher ΔT) can result in a narrower frequency pull-in range (lower1/ΔT). As phase shifts due to a frequency error can accumulate overtime, an accumulated phase error greater than 2 m radians can result inan “aliased” frequency error estimate that is not distinguishable from a“correct” frequency error estimate. The frequency pull-in range for afrequency error estimation algorithm using successive (or more generallyusing multiple time-separated) CIR estimates can be relatively narrowerthan other frequency error estimation algorithms described furtherherein that use information that is more restricted in time. The timeseparation ΔT between two success pilot sequences can be non-negligible.In a representative embodiment, pilot sequences can be separated by atime interval of 5 milliseconds, which can correspond to a pull-in rangeof 200 Hz. In another representative embodiment, pilot sequences can beseparated by a time interval of 0.5 milliseconds, which can correspondto a pull-in range of 2 kHz.

Another representative method to estimate frequency errors can exploit aprogressive phase ramping due to an accumulated frequency error. In someembodiments, a frequency error estimation method that provides for arelatively wider pull-in frequency range can be desired. A wider pull-infrequency range can be useful in certain operating conditions, e.g.,when a clock source has a relatively larger variance over time, or whenthe communication channel between the base station and the mobilewireless device is more rapidly time varying, such as can occur duringhigh speed movement. In addition, a wider pull-in frequency range can beuseful when providing for effective handovers of the mobile wirelessdevice between different cells of a wireless access network, or whenmeasuring neighbor cells to determine cells for selection and/or forre-selection.

In order to realize a frequency error estimation algorithm with a largerpull-in frequency range, the frequency error estimation algorithm canuse information accumulated over a shorter time interval than used abovewith successive (or multiple time-separated) CIR estimates. In arepresentative embodiment, an algorithm can exploit a phase ramping froma single pilot sequence or from a relatively shorter data sequenceinstead of relying on multiple pilot sequences or on multiple datasequences separated over longer periods of time. In a representativeembodiment, an estimate of a frequency error can be obtained usingEquations (3) and (4).

$\begin{matrix}{{\hat{f}}_{e} = {\arg \; {\max_{f_{e}}{{X^{*}D_{f_{e}}Y}}}}} & (3) \\{D_{f_{e}} = \begin{bmatrix}^{{j2\pi}\; f_{e}{T_{s} \cdot 0}} & 0 & \ldots & 0 \\0 & ^{{j2\pi}\; f_{e}{T_{s} \cdot 1}} & \ldots & \vdots \\\vdots & \ldots & \ddots & 0 \\0 & \ldots & 0 & ^{{j2\pi}\; f_{e}{T_{s} \cdot {({N - 1})}}}\end{bmatrix}} & (4)\end{matrix}$

In Equation (3), X can represent a reference signal vector, e.g., aknown transmitted sequence, while Y can represent a received signalvector, and the * operator can represent a Hermitian (complex conjugatevector) transpose operation. The unknown variable f_(e) can represent afrequency error for which different values can be searched to find anestimate {circumflex over (f)}_(e) that maximizes the function |X*D_(f)_(e) Y|. The time variable T_(s) can represent a time interval betweeneach sample of the reference signal vector X or between each sample ofthe received signal vector Y. In some embodiments, the samples of the(pilot or data) reference/received signal vector can be spacedequidistant in time. In some embodiments, a phase shift due to anaccumulation of the frequency difference between the transmitter of thewireless access network base station and the receiver of the mobilewireless device can increase proportionally to the time interval T_(s).Thus, each successive sample of the signal vector can be shifted by anadditional 2πf_(e)T_(s) radians. The diagonal matrix D_(f) _(e) canrepresent a complex-valued phase shift that applies for each of thereceived samples. A maximum likelihood frequency error estimate{circumflex over (f)}_(e) can be determined using Equations (3) and (4)by searching for a value of the frequency error estimate f_(e) thatmaximizes the function |X* D_(f) _(e) Y|. In a representativeembodiment, the maximum likelihood error estimation optimization metriccan be approximated by using the imaginary part of the product of areceived sample and the complex conjugate of a transmitted referencesample, i.e., by using Im{y_(k)·x_(k)*}, which in turn can beapproximated as kf_(e) to derive the discriminant.

In order to create a method that combines estimates provided by twodifferent frequency error estimation techniques, a balance can be soughtto use information generated by a “fine” frequency tracking loop and bya “coarse” frequency tracking loop that can operate in parallel. A“fine” frequency tracking loop can refer to a frequency error estimationmethod that has greater accuracy (higher processing gain) but a narrowerfrequency pull-in range. The “fine” frequency tracking loop can providefor accurate frequency tracking but can be unable to correct for largefrequency errors. The “fine” frequency tracking loop can be used for“fine” adjustment of the frequency tracking with high accuracy; however,large variations or jumps in frequency errors can be not tracked, as alarge frequency error change can fall outside of the pull-in range ofthe “fine” frequency tracking loop. A representative “fine” frequencytracking loop can be based on using pairs of (or more generallymultiple) CIR estimates as described hereinabove. A CIR-based frequencyerror estimation method as disclosed herein can provide a relativelyhigher processing gain (due to a corresponding to a pilot sequencelength) and a relatively narrower frequency pull-in range, the latterwhich can be limited by the time separation between two successive pilotsequences or data sequences from which the CIR estimates can be derived.A CIR-based frequency error estimation algorithm that uses multiple CIRestimates (derived from pilot sequences and/or from data sequences) canbe considered a representative “fine” frequency tracking loop. A“coarse” frequency tracking loop can refer to a frequency errorestimation method that has a relatively lower processing gains (worseaccuracy) but provides a wider frequency pull-in range. The frequencypull-in range of the “coarse” frequency tracking loop can be limited bythe time separation between two adjacent pilot samples (or between twodifferent data samples) instead of the larger time separation betweentwo sets of pilot sequences (or two different data sequences). Ingeneral, a representative “coarse” frequency error estimation method fora “coarse” frequency tracking loop can exploit a phase rotation thatoccurs from sample to sample within a pilot sequence (or within a datasequence). The “coarse” frequency error estimation method can provide alower processing gain than provided by a “fine” frequency errorestimation method, the latter being based on two (or more) timeseparated channel impulse response estimations. A linear combination orweighted combination of the frequency error estimations provided by a“coarse” frequency error estimation and by a “fine” frequency errorestimation in parallel may not achieve a “reasonable” frequency error orcan require a long time to converge to a correct value. Thus an“intelligent” combination of the frequency error estimates provided bythe “coarse” and “fine” frequency error estimations can be required toachieve higher accuracy with relatively fast convergence times.

During a “far cell” channel condition, which includes a relatively lowsignal to interference plus noise ratio (SINR) measured at the receiverof the mobile wireless device due to a high level of attenuation ofsignals from the base station of the wireless access network (or whenhigh levels of interference occurs), a “coarse” frequency errorestimation method can produce frequency error estimates with a largevariation in values. The large variation in values of individual“coarse” frequency error estimates can potentially exceed a frequencypull-in range of an accompanying “fine” frequency error estimationmethod. As a result of an inaccurate “coarse” frequency error estimate,the “fine” frequency error estimation method can converge to anincorrect “aliased” frequency error value (at least for a period of timeuntil the incorrect value is recognized and corrected for). Inaccurate“coarse” frequency error estimation can thus result in large overallfrequency error estimation fluctuations when combining information froma “coarse” frequency tracking loop (FTL) with a “fine” frequencytracking loop. Moreover, as a “fine” FTL can have a relatively narrowerpull-in frequency range, ignoring or disabling the “coarse” FTL canresult in a mobile wireless device remaining “stuck” at an incorrect“aliased” frequency estimation value (or within a particular range ofvalues), as the “fine” FTL cannot distinguish between an incorrect“aliased” frequency estimation value and a correct frequency estimationvalue (the pull-in frequency range of the “fine” FTL being relativelysmall). Without adjustments based on the “coarse” FTL, the mobilewireless device, in some circumstances, can drop voice connections orstall data transfers until a correct frequency error estimation isavailable. In order to achieve a stable frequency error estimation andtracking loop, a method to apply information derived from the “coarse”FTL in combination with a “fine” FTL, without introducing unwantedfrequency estimation fluctuations or false alarms, can be desired.

FIG. 2 illustrates a combination frequency tracking loop 200 thatincludes “fine” frequency estimation and “coarse” frequency estimationin parallel. As described for the FTL of FIG. 1, a set of receiveddigital samples can be adjusted by a frequency adjustment block 202 toproduct a set of frequency adjusted digital samples. All or a subset ofthe frequency adjusted digital samples can be provided to a “fine”frequency estimation block 204 and to a “coarse” frequency estimationblock 210 in parallel. It should be noted that the “fine” and “coarse”frequency error estimation blocks 204 and 210 respectively can operateon the same and/or on different sets of frequency adjusted digitalsamples. As described hereinabove, the “fine” frequency estimation block204 can use digitally adjusted samples spread out over a longer timeinterval that those used by the “coarse” frequency estimation block 210.Thus, updates to “fine” frequency estimates output by the “fine”frequency estimation block 204 can occur at a different rate thanupdates to “coarse” frequency estimates output by the “coarse” frequencyestimation block 210. Each of the estimation blocks 204/210 can produceindependent “fine” and “coarse” frequency error estimates. In someembodiments, the “fine” frequency estimation block 204 uses a “fine”frequency estimation method based on multiple CIR estimates derived frommultiple pilot sequences or derived from multiple data sequences asdescribed hereinabove. In some embodiments, the “coarse” frequencyestimation block 210 uses a “coarse” frequency estimation method basedon samples of a single pilot sequence or single data sequence as alsodescribed hereinabove. Outputs of the “fine” frequency estimation block204 can be scaled by a “fine” loop gain and passed through a “fine”frequency filter block 206 to produce a filtered “fine” frequencyestimate. Similarly, outputs of the “coarse” frequency estimation block210 can be scaled by a “coarse” loop gain and passed through a “coarse”frequency filter block 212 to produce a filtered “coarse” frequencyestimate. The filtered “fine” frequency estimates and the filtered“coarse” frequency estimates can be provided to a combination decisionblock 208 that can use information provided by both the “fine” and“coarse” frequency estimates to determine jointly a “combined” frequencyestimate. In a representative embodiment, the combination decision block208 uses the “coarse” frequency estimate to determine a range offrequencies within which to use the “fine” frequency estimates toconverge to a frequency error estimate as described further herein.

FIG. 3 illustrates a graph 300, representing a scalable combination offrequency error estimations provided by a fine frequency tracking loopand a coarse frequency tracking loop. The “fine” frequency errorestimation loop can provide multiple candidate “fine” frequency errorestimates among which to choose a frequency error estimate. Eachcandidate “fine” frequency error estimate can cover a range offrequencies, labeled as a fine frequency pull-in range in FIG. 3. The“coarse” frequency error estimation loop can provide a single “coarse”frequency error estimate, and the “coarse” frequency error estimate canbe associated with an accuracy that characterizes a range of frequenciesin which the coarse frequency error estimate can be considered accurate.When the frequency error estimation accuracy of the “coarse” frequencytracking loop is less than one-half of the pull-in frequency range ofthe “fine” frequency tracking loop, the combination decision block 208can select the closest “fine” frequency error estimate as the “combined”frequency error estimate. With a sufficiently accurate “coarse”frequency error estimate (i.e., a sufficiently narrow coarse frequencyaccuracy range), the closest “fine” frequency error estimate to the“coarse” frequency error estimate can be considered the “best” estimate.When the received signals at the mobile wireless device exceed a signalquality and/or signal strength threshold level, e.g., having an SINRabove a threshold value, or a signal strength above a threshold value,or a measured level of noise plus interference below threshold value, avariation of the “coarse” frequency estimates can be small (i.e., a lowvariance value). With high signal quality, one can conclude with arelatively high probability or success that the closest “fine” frequencyestimate to the “coarse” frequency estimate can be the correct value touse. Combining information from the “coarse” frequency estimate and fromthe “fine” frequency estimates can result in accurate, scalable andadaptive frequency estimation and tracking The “coarse” frequencyestimates can be used to select to a range of frequencies over which the“fine” frequency estimates can be used to converge to an accuratefrequency estimate. In some embodiments, when the “coarse” frequencyestimate accuracy range exceeds more the one-half the “fine” frequencypull-in range, multiple candidate “fine” frequency error estimates canbe possible, a thus a set of “fine” frequency error estimates canprovide multiple hypotheses by which to test and seek to converge thefrequency error estimation and tracking loop. In some embodiments, the“coarse” frequency tracking loop can be considered an “outer” loop,which provides a range of frequencies to search, while the “fine”frequency tracking loop can be considered an “inner” loop, which can beused to converge to a final frequency value. In some embodiments, the“inner” “fine” frequency loop can be run continuously to estimate andtrack frequency errors, while the “outer” “coarse” frequency loop can beenabled to run in parallel as required, e.g., to generate an initialestimate upon an initial connection and/or based on determination of ajump in frequency error. In some embodiments, the “coarse” frequency“outer” loop can be enabled adaptively based on a physical layer metric,e.g., an SINR value falling below a threshold, a frequency errorestimate change exceeding a threshold, a frequency error accuracyestimate exceeding a threshold, a bit error rate (BER) or block errorrate (BLER) exceeding a threshold, or other combination of physicallayer metrics. In some embodiments, the “coarse” frequency errorestimate and tracking loop can be not enabled continuously to minimizecomputational requirements and thereby conserve power. In someembodiments, the “coarse” frequency tracking loop and the “fine”frequency tracking loop can each use a different time constant, e.g.,the “coarse” frequency tracking loop being operable less frequently thanthe “fine” frequency tracking loop. An accumulation of a frequencytracking error of sufficient magnitude to require the use of the“coarse” frequency tracking loop can take a longer time to occur, andthus the “coarse” frequency tracking loop can be updated less oftenwithout a substantial loss in performance.

In some embodiments, combining information from the “fine” frequencyerror tracking loop and the “coarse” frequency error tracking loops canbe biased to use information from the “coarse” frequency error trackingloop initially when a large frequency error can occur and then adjustedto use information from the “fine” frequency error tracking loop toconverge to an accurate frequency error value. As a representativeexample, a mobile wireless device in use on a high speed train (and thustravelling at a relatively rapid rate through multiple cells withchanging directions relative to base stations in each cell), a Dopplershift in frequency can change as the mobile wireless device traversewithin a cell (passing the base station) and/or when traversing betweencells (and thus moving away from one base station and toward a secondbase station). A slowly converging, but highly accurate, narrowfrequency pull-in range “fine” frequency tracking loop can be inadequateto deal with rapid frequency changes that can occur in the describedscenario, and thus a “coarse” frequency tracking loop can be used asrequired to “adjust” and “realign” frequency error estimates, followedby use of the “fine” frequency error tracking loop to refine an estimateprovided by the “coarse” frequency error tracking loop. In someembodiments, the “coarse” frequency error tracking loop can be used whenan instantaneous (or filtered) frequency error estimate jumps by morethan a threshold value.

FIG. 4 illustrates a graph 400, representing another scalablecombination of frequency error estimations from a fine frequencytracking loop and a coarse frequency tracking loop. In the scenarioillustrated in FIG. 4, a frequency error estimation accuracy of the“coarse” frequency tracking loop is nearly equal to or more than half ofthe pull-in range of the “fine” frequency tracking loop. As such, therecan be more than one possible “fine” frequency estimate that is “close”to the “coarse” frequency estimate. The true frequency error can beambiguous, as multiple hypotheses for the combined frequency errorestimate can be plausible based on the information provided by the“coarse” and “fine” frequency estimates. As illustrated in FIG. 4, the“coarse” frequency estimate can be nearly equidistant from two different“fine” frequency estimates. In some embodiments, the “coarse” frequencyestimate can be closer to one of the “fine” frequency estimates, but thevariation in values for the “coarse” frequency estimate can vary morethan half of the pull-in frequency range of the “fine” frequencyestimates, so that the accuracy of the “coarse” frequency estimate isnot inadequate to determine which of the “fine” frequency estimates tochoose. In a representative embodiment, a “combined” frequency errorestimation algorithm can use a “conservative” hypothesis, e.g., based onexamining a most recent past history of frequency error estimates, anduse the “fine” frequency tracking loop to converge to a more accurateestimate of the frequency error over time. While this process can takelonger than the scenario illustrated in FIG. 3, the disclosed algorithmcan still largely reduce the frequency tracking convergence time whencompared to starting from a linear combination of fine and coarsefrequency tracking loops.

In a representative embodiment, combining information from “fine” and“coarse” frequency estimates, e.g., by the combination decision block208 of FIG. 2, can be adaptive depending on a scale of an existingfrequency error or an amount of residual frequency error that needs tobe corrected. In some cases, the coarse frequency tracking loop may notbe needed, e.g., once converged to a correct range of frequencies thatcan be tracked by the “fine” frequency tracking loop, the “coarse”frequency estimate can provide only a “redundant” repeated result thatconfirms the current range of frequencies used by the “fine” frequencytracking loop. As the “coarse” frequency estimation can require extracomputational overhead, the “coarse” frequency estimate can be invokedonly as required, or at less frequent time intervals. In this case, itcan be advantageous to determine only “fine” frequency estimates(essentially resulting in a “fine” frequency tracking loop only, whilesuspending the “coarse” frequency tracking loop until required). The“coarse” frequency estimates can be resumed when stability or accuracyof the “fine” frequency estimates is suspect, e.g., when the SINRdegrades, or when a sudden change in frequency error estimation valuesindicates that the “fine” frequency error estimate may have converged toan incorrect aliased frequency estimate rather than to the correctfrequency estimate. In another embodiment, a rate at which the coarsefrequency error estimates are computed can be scaled down compared withhow often the fine frequency error estimates are computed, i.e., can beperformed less frequently.

In some scenarios, e.g., when a high received SINR exists, a “coarse”frequency estimation loop can be used initially to determine rapidly arange of frequencies over which a “fine” frequency estimation loop canbe used to converge accurately to track frequency variation. In somescenarios, e.g., when a low received SINR exists, the “coarse” frequencyestimation loop can provide widely varying values, e.g., can indicatedifferent ranges of frequencies over which to use the “fine” frequencyestimation loop. The “noisy” coarse frequency estimates can negativelyimpact the performance of the mobile wireless device, as sudden changesin frequency can be selected that affects decoding results for receiveddata. With inadequate SINR, it can be difficult to guarantee a coarsefrequency tracking loop estimate variation that is less than one-half ofthe pull-in frequency range of the fine frequency tracking loop. Ifadditional pilot (or data) sequence information is available than isused “normally” by the tracking loops, then in some scenarios, it can bepossible to use longer pilot (or data) sequences for the coarsefrequency tracking loop (thereby increasing processing gain and loweringthe variation of the coarse frequency estimate) or to use morefrequently occurring (closer in time spaced) pilot (or data) sequencesto increase the pull-in frequency range of the fine frequency trackingloop. In some scenarios, however, the pilot sequence structure is fixedby a communication protocol standard. Thus, in some embodiments, alength of the sequences and/or a spacing between the sequences can befixed. Variation in the coarse frequency estimates can be thus limitedby the length of the pilot sequences available to use. The quality ofthe received pilot sequence can also be determined by variablecommunication channel conditions (e.g., weak received signals and/orhigh noise/interference). Although the probability distribution function(pdf) of the coarse frequency estimates can indicate a high probabilityof the coarse frequency estimates being within the desired pull-in rangeof the fine frequency tracking loop, the “tails” of the pdf can resultin a non-trivial probability of the coarse frequency estimate beingoutside of a desired pull-in range. To address the infrequent occurrenceof outlying coarse frequency estimate values, the combination decisionblock 208 can introduce a time hysteresis, a frequency hysteresis,and/or a strength based decision to improve the robustness of thefrequency error estimation algorithm.

In a representative embodiment, a time hysteresis can be introduced tocontrol when the coarse frequency error estimation information iscombined with the fine frequency error estimation. In an embodiment, atrigger condition can be established based on a magnitude of thedifference between the coarse frequency error estimate and the finefrequency error estimate. When the magnitude of the difference is morethan a pre-determined value, a trigger can occur. In some embodiments,the pre-determined value for the magnitude of the difference that“triggers” the counter can be based on the pull-in frequency range ofthe fine frequency error estimation and tracking loop. In an embodiment,the combined frequency error estimate can use the coarse frequency errorestimate in combination with the fine frequency error estimate only whenN consecutive triggers occur. If the probability of a single “false”alarm (due to an incorrect, outlying coarse frequency estimate value inthe “tails” of the pdf) is p, then the probability of N consecutivefalse alarms can be represented as p^(N). Higher values of N (i.e., moreconsecutive triggers) can reduce the probability of incorrectlyswitching frequency estimates; however, the time to correctly switch canbe increased by the time to receive N consecutive triggers. In anotherembodiment, the combined frequency error estimate can use the coarsefrequency error estimate in combination with the fine frequency errorestimate when M triggers occur out of N consecutive times, where M<N. Inanother embodiment, after the mobile wireless communication device has“switched” between two different “fine” frequency estimate hypotheses,e.g., when a relatively large jump in frequency estimate occurs, thecombination decision block 208 can use the fine frequency trackingestimates only following the jump, and/or can remain on the “new”frequency for a pre-determined time duration T, thereby prohibiting aswitch to another “hypothesis” fine frequency estimate until after thetime duration T elapses. A time duration to remain in the range of thenew frequency estimate can be adapted as well based on a measure of theaccuracy and/or the strength of the coarse frequency estimate(s) thatprecipitated the switch. In an embodiment, the combination decisionblock 208 can use fine frequency estimates only after a switch for aperiod of time≧NΔT_(c), where T_(c) can represent a time betweensuccessive coarse frequency error estimates, and a value for N selectedfor the “lock out” time period can be adjusted based on the “strength”and/or “accuracy” and/or a confidence measure of the coarse frequencyestimate(s). In each of the embodiments described, the threshold forswitching can be increased compared with using the “coarse” frequencyestimates individually.

In a representative embodiment, a frequency hysteresis can be introducedto control when the coarse frequency error estimation information iscombined with the fine frequency error estimation. In an embodiment, arange of frequencies at and close to the “mid-point” between twodifferent “fine” frequency estimates is considered as “unreliable” bythe combination decision block 208. In an embodiment, the combinationdecision block can require that the coarse frequency estimate be outsideof a range of frequencies spanning

[f_(mid)−Δf_(hys), f_(mid)+Δf_(hys)]

in order to be considered for combination with the “fine” frequencyestimates. The frequency hysteresis can “bias” the combination decisionblock 208 to use the “current” fine frequency hypothesis value until the“coarse” frequency estimate is “significantly” closer to an adjacent (ordifferent) fine frequency hypothesis value. In an embodiment, timehysteresis and frequency hysteresis can be combined, so that the“coarse” frequency estimate must be repeatedly (and/or frequently) in arange much closer to an adjacent (or different than current) finefrequency hypothesis value in order to indicate a switch of frequencies.

In a representative embodiment, a measure of “strength” of the coarsefrequency error estimate can be used to limit false alarms. The strengthmeasure can reflect a confidence in the accuracy of the coarse frequencyerror estimate. In an embodiment, the strength measure can be a relativemeasure of the processing gain of the coarse frequency error estimate.The coarse frequency error estimate can be used (or considered for use)with the fine frequency error estimate by the combination decision block208 when the strength measure exceeds a pre-determined strengththreshold value. In some embodiments, the strength threshold test can becombined with time hysteresis and/or frequency hysteresis. In anembodiment, the time hysteresis values, e.g., a value for N and/or Mdescribed earlier, can be adjusted based on a measure of the strength ofthe coarse frequency error estimates. For “higher” strength coarsefrequency error estimates, the number of repeated triggers N (or M outof N) can be adjusted lower reflecting increased confidence in thecoarse frequency error estimates. For “lower” strength coarse frequencyerror estimates, the number of repeated triggers N (or M out of N) canbe adjusted higher reflecting lowered confidence in the coarse frequencyerror estimates, and thereby requiring additional triggers to ensure ahigher probability of a correct decision when combining the coarsefrequency error estimate with the fine frequency error estimate in thecombination decision block 208. In an embodiment, the frequencyhysteresis values, e.g., the width of the frequency range of thefrequency estimates that is considered “suspect” or “blocked” from usingthe coarse frequency estimate can be narrower for higher strength valuesof the coarse frequency estimate and wider for lower strength values ofthe coarse frequency estimate. The combination decision block 208 canthus adjust to variable coarse frequency estimates based on an estimateof the strength and/or the persistence of values for the coarsefrequency estimate. When the coarse frequency estimate has a lowerstrength (or confidence) value, the combination decision block 208 canconsider additional information, e.g., more coarse frequency estimates,in order to use the coarse frequency estimates in determining the“combined” frequency estimate. When the coarse frequency estimate has ahigher strength (or confidence) value, the combination decision block208 can require fewer coarse frequency estimates (or accept a broaderrange of frequency values for the coarse frequency estimate) in order toinclude the coarse frequency estimate in combination with the finefrequency estimate to determine the “combined” frequency estimate.

Scalable and adaptive frequency estimation and tracking algorithmsdisclosed herein offer many advantages over conventional frequencyestimation and tracking algorithms. A conventional frequency estimationalgorithm that linearly combines the frequency error estimations fromdifferent frequency error estimations algorithms can be sub-optimal, asthe different frequency error estimation algorithms can have differentestimation accuracy and pull-in frequency ranges. The disclosedfrequency estimation and tracking algorithms provided herein canoptimally combine the frequency error estimation from both fine andcoarse frequency tracking loops while considering the pull-in range andestimation accuracy of these algorithms. Furthermore, adaptivealgorithms can provide more accurate frequency error estimation,especially when higher levels of frequency error variation require bothfine and coarse frequency tracking loop results to be effectively used.Finally, adaptive algorithms can provide much faster convergence timesand better frequency tracking capabilities in dynamic scenarios such ashigh-speed train scenarios when compared with conventional algorithms.

FIG. 5 illustrates a diagram 500 of a flowchart for a representativemethod to combine fine and coarse frequency estimates in a mobilewireless device in accordance with some embodiments. In step 502, acoarse frequency error estimate is determined. In some embodiments, thecoarse frequency error estimate is determined based on a measure ofphase ramping that can be caused by a sampling frequency error between atransmitter (that sent a reference signal or data) and a receiver (thatmeasured and equalized data). In step 504, a plurality of fine frequencyerror estimates are obtained. In some embodiments, a fine frequencytracking loop estimate provides the plurality of fine frequency errorestimates using a CIR-based frequency estimation technique. In step 506,the one or more candidate fine frequency error estimates are determinedfrom the plurality of fine frequency error estimates. In someembodiments, the one or more candidate fine frequency error estimatesinclude a set of fine frequency error estimates closest to the coarsefrequency error estimate. In some embodiments, an accuracy range of thecoarse frequency error estimate determines a range of frequencies acrosswhich to select the fine frequency error estimates. In step 508, thecoarse frequency error estimate can be combined with the set ofcandidate fine frequency error estimates to provide a frequency errorsignal. In some embodiments, the combination can account for a measureof signal quality, e.g., an SINR value, when combining informationprovided by the coarse frequency error estimate and the set of candidatefine frequency error estimates. In step 410, the frequency error signalcan be used to determine an adjustment value for a frequency rotatorthat adjusts received digital samples at the receiver or a frequencycorrection signal for a voltage controlled crystal oscillator thataffects the digital sampling of received analog radio frequencywaveforms at the mobile wireless device. The frequency adjustment valueand/or the VCXO control signal can be applied to the frequency rotatoror the VCXO to correct for an estimated frequency error.

FIG. 6 illustrates a flowchart detailing a method 600 for combining fineand coarse frequency estimation results in accordance with the describedembodiments. In step 602, a coarse frequency error estimate isdetermined, e.g., based on a single pilot sequence or data sequence asdescribed herein. In step 604, a plurality of fine frequency errorestimates are determined, e.g., using a set of channel impulse responseestimates derived from two or more pilot sequences or data sequences. Instep, 606, a candidate fine frequency error estimate is determined asthe fine frequency error estimate in the set of plurality of finefrequency error estimates closest to the coarse frequency errorestimate. A distance between the coarse and fine frequency errorestimates can be measured in Hz or in another equivalent frequencymetric. In step 608, a confidence metric for the coarse frequency errorestimate is calculated. The confidence metric can be based on a timehistory of recent coarse frequency error estimates, or based on afrequency distance of the coarse frequency error estimate from one ormore frequency error estimates in the plurality of fine frequency errorestimates, or based on a strength of the coarse frequency errorestimates, or based on a combination of these. In step 610, thecalculated confidence metric is compared to a pre-determined thresholdvalue. The pre-determined threshold value can be time-based,frequency-based, strength-based, or a combination of such measures. Instep 612, when the confidence metric for the coarse frequency errorestimate exceeds the threshold value, a frequency adjustment isdetermined based on the candidate fine frequency error estimate. In step614, when the confidence metric for the coarse frequency error estimatedoes not exceed the threshold value, the frequency adjustment isdetermined based on a fine frequency error estimate in the plurality offine frequency error estimates that is closest to a most recent previousfine frequency error estimate. The method described in FIG. 6 providesfor a robust determination of frequency adjustments based on acombination of coarse frequency error estimates and fine frequency errorestimates.

FIG. 7 illustrates a diagram 700 of a representative set of signalprocessing and/or data processing elements that can be included in amobile wireless device in which the frequency error estimation andtracking methods described herein are embodied. In some embodiments, themobile wireless device includes one or more transceivers 716, which caninclude one or more transmitters and one or more receivers. Thetransceivers 716 can be included as part of a set of wireless circuitryin the mobile wireless device. A transmitter section of a transceivercan include portions of the wireless circuitry in the wirelesscommunication device that transforms (e.g., modulates and encodes)digital packets into analog radio frequency waveforms and transmits theRF waveforms via one or more antennas to a base station of a wirelessaccess network. Similarly, a receiver section of a transceiver caninclude a portion of the wireless circuitry in the mobile wirelessdevice that receives RF waveforms through one or more antennas andtransforms (e.g., demodulates and decodes) the RF waveforms into digitalpackets The mobile wireless device can include wireless circuitry thatcan provide for communication with a wireless network, e.g., an LTEwireless network. The wireless circuitry in the mobile wirelesscommunication device can include processing circuitry 710, e.g.,processor 712 coupled to memory 714, and additional wireless circuitry,e.g., transceivers 716, to transmit and receive wireless signalsaccording to various wireless communication protocols. A wirelesscircuitry module (which can also be referred to as a wireless subsystem)of the mobile wireless device 102 include transmitters and receivers toprovide signal processing of radio frequency wireless signals formattedaccording to wireless communication protocols, e.g., according to an LTEwireless communication protocol, or another cellular wirelesscommunication protocol. In some embodiments, the wireless circuitrymodule can include components such as: processors and/orspecific-purpose digital signal processing (DSP) circuitry forimplementing functionality such as, but not limited to, baseband signalprocessing, physical layer processing, data link layer processing,and/or other functionality; one or more digital to analog converters(DACs) for converting digital data to analog signals; one or more analogto digital converters (ADCs) for converting analog signals to digitaldata; radio frequency (RF) circuitry (e.g., one or more amplifiers,mixers, filters, phase lock loops (PLLs), and/or oscillators); and/orother components. The wireless circuitry module can be also referred toas a radio in some embodiments. The processing circuitry 710 can operatein combination with the transceivers 716 to perform one or more aspectsof the frequency error estimation and tracking methods described herein.

The various aspects, embodiments, implementations or features of thedescribed embodiments can be used separately or in any combination.Various aspects of the described embodiments can be implemented bysoftware, hardware or a combination of hardware and software. Thedescribed embodiments can also be embodied as computer program codeencoded on a non-transitory computer readable medium for controllingoperation of a wireless communication device. The computer readablemedium is any data storage device that can store data which canthereafter be read by a computer system. Examples of the computerreadable medium include read-only memory, random-access memory, CD-ROMs,HDDs, DVDs, magnetic tape, and optical data storage devices. Thecomputer readable medium can also be distributed over network-coupledcomputer systems so that the computer program code is stored andexecuted in a distributed fashion.

The foregoing description, for purposes of explanation, used specificnomenclature to provide a thorough understanding of the describedembodiments. However, it will be apparent to one skilled in the art thatthe specific details are not required in order to practice the describedembodiments. Thus, the foregoing descriptions of specific embodimentsare presented for purposes of illustration and description. They are notintended to be exhaustive or to limit the described embodiments to theprecise forms disclosed. It will be apparent to one of ordinary skill inthe art that many modifications and variations are possible in view ofthe above teachings.

What is claimed is:
 1. A method to estimate frequency errors anddetermine a frequency adjustment in a mobile wireless device, the methodcomprising: in the mobile wireless device: determining a coarsefrequency error estimate; determining a plurality of fine frequencyerror estimates; selecting at least one candidate fine frequency errorestimate in the plurality of fine frequency error estimates, the atleast one candidate fine frequency error estimate having a frequencyvalue closest to a corresponding frequency value for the coarsefrequency error estimate; and determining a frequency adjustment basedon a combination of the coarse frequency error estimate and the selectedat least one candidate fine frequency error estimate.
 2. The method ofclaim 1, further comprising: calculating a confidence metric for thecoarse frequency error estimate; comparing the confidence metric to apre-determined threshold value; when the confidence metric exceeds thepre-determined threshold value, determining the frequency adjustmentbased on the candidate fine frequency error estimate; and when theconfidence metric does not exceed the pre-determined threshold value,determining the frequency adjustment based on a fine frequency errorestimate in the plurality of fine frequency error estimates that isclosest to a most recent previous fine frequency error estimate.
 3. Themethod of claim 1, wherein determining the coarse frequency errorestimate comprises obtaining a maximum likelihood frequency error valuethat maximizes a discriminant function based on a set of samplesreceived in a single orthogonal frequency division multiplexing (OFDM)symbols.
 4. The method of claim 3, wherein the set of samples of thesingle OFDM symbol comprise a reference signal transmitted by an evolvedNodeB of a wireless network operating in accordance with a Long TermEvolution (LTE) wireless communication protocol.
 5. The method of claim1, wherein determining the plurality of fine frequency error estimatescomprises obtaining a set of frequency error values based on at leasttwo channel impulse response estimates derived from samples received intwo different orthogonal frequency division multiplexing (OFDM) symbols.6. The method of claim 5, wherein the two different OFDM symbolscomprise two reference signals transmitted at least one sub-frame apartin time by an evolved NodeB of a wireless network operating inaccordance with a Long Term Evolution (LTE) wireless communicationprotocol.
 7. The method of claim 1, further comprising: determining anaccuracy range for the coarse frequency error estimate; comparing theaccuracy range to a frequency pull-in range for the plurality of finefrequency error estimates; and when the accuracy range of the coarsefrequency error estimate is less than one-half the frequency pull-inrange of the plurality of fine frequency error estimates, determiningthe frequency adjustment based on a particular fine frequency errorestimate in the plurality of fine frequency error estimates that isclosest to the coarse frequency error estimate.
 8. The method of claim1, wherein determining the plurality of fine frequency error estimatescomprises calculating a set of filtered discriminants based on aplurality of successive received pilot sequences.
 9. The method of claim2, wherein the confidence metric comprises a measure of a receivedsignal quality or signal strength for a reference signal received by themobile wireless device.
 10. The method of claim 2, wherein theconfidence metric comprises a measure of a bit error rate or a blockerror rate calculated by the mobile wireless device.
 11. The method ofclaim 1, further comprising: applying the frequency adjustment to afrequency rotator that adjusts digital samples of radio frequencywaveforms received by a transceiver in the mobile wireless device. 12.The method of claim 1, further comprising: applying the frequencyadjustment to a voltage controlled crystal oscillator (VCXO) thatcontrols sampling of radio frequency waveforms by a transceiver in themobile wireless device.
 13. A mobile wireless device comprising:wireless circuitry including at least one transceiver; one or moreprocessors coupled to the wireless circuitry; and a memory coupled tothe one or more processors, wherein the one or more processors areconfigured to execute computer-executable instructions stored within thememory to cause the mobile wireless device to: determine a coarsefrequency error estimate; determine a plurality of fine frequency errorestimates; calculate a confidence metric for the coarse frequency errorestimate; compare the confidence metric to a threshold value; when theconfidence metric exceeds the threshold value, determine a frequencyadjustment based on a candidate fine frequency error estimate in theplurality of fine frequency error estimates that is closest to thecoarse frequency error estimate; and when the confidence metric does notexceed the threshold value, determine the frequency adjustment based ona fine frequency error estimate in the plurality of fine frequency errorestimates that is closest to a most recent previous fine frequency errorestimate.
 14. The mobile wireless device of claim 13, wherein the one ormore processors cause the mobile wireless device to determine the coarsefrequency error estimate by obtaining a maximum likelihood frequencyerror value that maximizes a discriminant function based on a set ofsamples received in a single orthogonal frequency division multiplexing(OFDM) symbols.
 15. The mobile wireless device of claim 14, wherein theset of samples of the single OFDM symbol comprise a reference signaltransmitted by an evolved NodeB of a wireless network operating inaccordance with a Long Term Evolution (LTE) wireless communicationprotocol.
 16. The mobile wireless device of claim 13, wherein the one ormore processors cause the mobile wireless device to determine theplurality of fine frequency error estimates by obtaining a set offrequency error values based on at least two channel impulse responseestimates derived from samples received in two different orthogonalfrequency division multiplexing (OFDM) symbols.
 17. The mobile wirelessdevice of claim 16, wherein the two different OFDM symbols comprise tworeference signals transmitted at least one sub-frame apart in time by anevolved NodeB of a wireless network operating in accordance with a LongTerm Evolution (LTE) wireless communication protocol.
 18. The mobilewireless device of claim 13, wherein the one or more processors causethe mobile wireless device to determine the plurality of fine frequencyerror estimates by calculating a set of filtered discriminants based ona plurality of successive received pilot sequences.
 19. The mobilewireless device of claim 13, wherein the confidence metric comprises ameasure of a received signal quality or signal strength for a referencesignal received by the mobile wireless device.
 20. A non-transitorycomputer readable medium having computer program code encoded thereon,the computer program code, when executed by one or more processors,causes a mobile wireless device to: determine a coarse frequency errorestimate; determine a plurality of fine frequency error estimates;select at least one candidate fine frequency error estimate in theplurality of fine frequency error estimates, the at least one candidatefine frequency error estimate having a frequency value that is closestto a corresponding frequency value for the coarse frequency errorestimate; and determine a frequency adjustment based on a combination ofthe coarse frequency error estimate and the selected at least onecandidate fine frequency error estimate.